openai-gpt-image-mcp Details
A Model Context Protocol (MCP) tool server designed for OpenAI's GPT-4o and gpt-image-1 image generation and editing APIs. This MCP server exposes image-generation capabilities via two primary tools, create-image and edit-image, enabling developers to generate images from prompts and perform inpainting, outpainting, or compositing edits with fine-grained prompt control. It also provides file-output options so generated content can be saved to disk or returned as base64, and it supports a range of MCP-compatible clients, including Claude Desktop, Cursor, VSCode, Windsurf, among others. Built on the MCP SDK and OpenAI and OpenAI-compatible tooling, this server offers a ready-to-run solution for integrating image APIs into MCP-enabled workflows.
Use Case
This MCP server provides a compact, pluggable endpoint for generating and editing images via MCP clients. It exposes two concrete MCP tools: create-image and edit-image. The server handles requests from MCP clients and returns image data either as base64 or via file paths, depending on payload size and client configuration. The configuration examples demonstrate how to wire the MCP into Claude Desktop or VS Code, including optional Azure deployment options. Example usage from the documentation shows how to configure and run the server, making it straightforward to integrate OpenAI image APIs into your MCP-based tooling. Key snippets include installation steps, MCP server configuration, and environment-variable-based deployment for Azure or local environments.
Available Tools (2)
Examples & Tutorials
Installation:
git clone https://github.com/SureScaleAI/openai-gpt-image-mcp.git
cd openai-gpt-image-mcp
yarn install
yarn buildConfiguration (Claude Desktop / VS Code):
{
"mcpServers": {
"openai-gpt-image-mcp": {
"command": "node",
"args": ["/absolute/path/to/dist/index.js"],
"env": { "OPENAI_API_KEY": "sk-..." }
}
}
}Azure deployment example:
{
"mcpServers": {
"openai-gpt-image-mcp": {
"command": "node",
"args": ["/absolute/path/to/dist/index.js"],
"env": {
"AZURE_OPENAI_API_KEY": "sk-...",
"AZURE_OPENAI_ENDPOINT": "my.endpoint.com",
"OPENAI_API_VERSION": "2024-12-01-preview"
}
}
}
}Env-file usage:
{
"mcpServers": {
"openai-gpt-image-mcp": {
"command": "node",
"args": ["/absolute/path/to/dist/index.js", "--env-file", "./deployment/.env"]
}
}
}Run locally:
node dist/index.jsInstallation Guide
1) Clone the repository and install dependencies:
git clone https://github.com/SureScaleAI/openai-gpt-image-mcp.git
cd openai-gpt-image-mcp
yarn install
yarn build2) Run the MCP server:
node dist/index.jsIntegration Guides
Frequently Asked Questions
Is this your MCP?
Claim ownership and get verified badge
Sponsored
1MB Payload Limit: MCP clients (including Claude Desktop) have a hard 1MB limit for tool responses. Large images (especially high-res or multiple images) can easily exceed this limit if returned as base64. Auto-Switch to File Output: If the total image size exceeds 1MB, the tool will automatically save images to disk and return the file path(s) instead of base64. Default File Location: If you do not specify a file_output path, images will be saved to /tmp (or the directory set by MCP_HF_WORK_DIR) with a unique filename. Environment Variable: MCP_HF_WORK_DIR: Set this to control where large images and file outputs are saved. Best Practice: For large or production images, always use file output and ensure your client is configured to handle file paths.
OPENAI_API_KEY must be valid and have image API access. You must have a verified OpenAI organization. File paths must be absolute. When outputting files, ensure the directory is writable. For Azure deployments, provide AZURE_OPENAI_API_KEY, AZURE_OPENAI_ENDPOINT, and OPENAI_API_VERSION as needed. You can also supply an environment file via --env-file.
Compare Alternatives
Similar MCP Tools
9 related toolsPlaywright MCP
Playwright MCP server. A Model Context Protocol (MCP) server that provides browser automation capabilities using Playwright. This server enables large language models (LLMs) to interact with web pages through structured accessibility snapshots, bypassing the need for screenshots or visually-tuned models. The server is designed to be fast, lightweight, and deterministic, offering LLM-friendly tooling and a rich set of browser automation capabilities via MCP tools. It supports standalone operation, containerized deployments, and integration with a variety of MCP clients (Claude Desktop, VS Code, Copilot, Cursor, Goose, Windsurf, and others).
Sequential Thinking MCP Server
Sequential Thinking MCP Server provides a dedicated MCP tool that guides problem-solving through a structured, step-by-step thinking process. It supports dynamic adjustment of the number of thoughts and allows revision and branching within a controlled workflow, making it ideal for complex analysis and solution hypothesis development. This server is designed to register a single tool, sequential_thinking, and is integrated with common MCP deployment methods (NPX, Docker) as well as editor integrations like Claude Desktop and VS Code for quick setup. The documentation provides exact configuration snippets, usage patterns, and building instructions to help you deploy and use the MCP server effectively, including Codex CLI, NPX, and Docker installation examples.
N8N MCP Server
An MCP (Model Context Protocol) server designed to integrate Claude Desktop, Claude Code, Windsurf, and Cursor with n8n workflows. This MCP enables users to build, test, and orchestrate complex workflows by exposing a set of tools that bridge Claude’s capabilities with n8n’s automation platform. The project emphasizes robust trigger handling, multi-tenant readiness, and progressive documentation to help developers understand how tools map to real-world workflow tasks. It also outlines future tooling integration points (such as getNodeEssentials and getNodeInfo) to further enhance node-structure awareness within MCP-powered automations.
Hugging Face MCP Server
Hugging Face Official MCP Server connects your large language models (LLMs) to the Hugging Face Hub and thousands of Gradio AI Applications, enabling seamless MCP (Model Context Protocol) integration across multiple transports. It supports STDIO, SSE (to be deprecated but still commonly deployed), StreamableHTTP, and StreamableHTTPJson, with the Web Application allowing dynamic tool management and status updates. This MCP server is designed to be run locally or in Docker, and it provides integrations with Claude Desktop, Claude Code, Gemini CLI (and its extension), VSCode, and Cursor, making it easy to configure and manage MCP-enabled tools and endpoints. Tools such as hf_doc_search and hf_doc_fetch can be enabled to enhance document discovery, and an optional Authenticate tool can be included to handle OAuth challenges when called.
Shadcn UI MCP Server v4
Shadcn UI v4 MCP Server is an advanced MCP (Model Context Protocol) server designed to give AI assistants comprehensive access to shadcn/ui v4 components, blocks, demos, and metadata. It enables multi-framework support (React, Svelte, Vue, and React Native) with fast, cache-friendly access to component source code, demos, and directory structures, empowering AI-driven development workflows. The project emphasizes production-readiness with Docker Compose, SSE transport for multi-client deployments, and smart caching to optimize GitHub API usage while providing rich metadata and usage patterns for rapid prototyping and learning across frameworks.
Figma MCP server
The Figma MCP server enables design context delivery from Figma files to AI agents and code editors, empowering teams to generate code directly from design selections. It supports both a remote hosted server and a locally hosted desktop server, allowing seamless integration with popular editors through Code Connect and a suite of tools that extract design context, metadata, variables, and more. This guide covers enabling the MCP server, configuring clients (VS Code, Cursor, Claude Code, and others), and using a curated set of MCP tools to fetch structured design data for faster, more accurate code generation. It also explains best practices, prompts, and integration workflows that help teams align generated output with their design systems. The documentation includes concrete JSON examples for configuring servers in editors like VS Code and Cursor, as well as command examples for Claude Code integration and plugin installation.
MarkItDown MCP
MarkItDown-MCP is a lightweight MCP (Model Context Protocol) server provided as the markitdown-mcp package. It exposes a STDIO, Streamable HTTP, and SSE MCP server designed for calling MarkItDown to convert content to Markdown. The package focuses on simplicity and accessibility, enabling you to run the MCP server locally via a simple CLI, or in Docker for containerized workflows, with integration options for Claude Desktop. The core capability is exposed through a single tool, convert_to_markdown(uri), which accepts a URI in http:, https:, file:, or data: schemes to fetch content and convert it to Markdown. This MCP server is easy to install with pip and can be used in various transport modes, including STDIO and HTTP/SSE, making it a flexible choice for automations and integrations.
Chrome MCP Server
Chrome MCP Server is a Chrome extension-based Model Context Protocol (MCP) server that exposes your Chrome browser functionality to AI assistants like Claude, enabling complex browser automation, content analysis, and semantic search. It leverages your existing Chrome environment, including login states and configurations, to allow large language models and chatbots to control the browser natively without needing to launch a separate automation process. The project emphasizes privacy by remaining fully local and offers capabilities such as cross-tab context, streamable HTTP communication, and a built-in vector database for semantic search and content analysis. As an early-stage project, it includes a growing set of tools for browser control, inspection, and automation, with ongoing development to broaden compatibility and features.
MCP server for Appwrite docs
The MCP server for Appwrite docs enables LLMs and code-generation tools to interact with comprehensive Appwrite documentation. It empowers AI assistants to access up-to-date API references, SDK guides, and implementation examples, facilitating intelligent code generation, troubleshooting, and best-practice guidance directly from the official docs. This MCP brings real-time context, semantic search, and seamless integration with popular editors and IDEs to accelerate development workflows around Appwrite's APIs and SDKs.